Question Details

(solution) Journal of Experimental Psychology: Human Learning and

Here attached is an article on Arthur Reber's 1976 experiment. Can you please explain the theory behind the experiment, the hypothesis (including independent and dependent variable) (the rationale behind the experiment), the procedure, what information did Reber found, the results and why did it happen?

Journal of Experimental Psychology:


Human Learning and Memory


1976, Vol. 2, No. 1, 88-94 Implicit Learning of Synthetic Languages:


The Role of Instructional Set


Arthur S. Reber


Brooklyn College


The effect of instructional set on implicit learning of a synthetic language


was explored. Specifically, the neutral, implicit instructions used in previous studies were compared with explicit instructions which directed subjects to search for the complex rules that determined letter orderings. The


subjects given the explicit instructions were poorer at memorizing exemplars


from the language, learned less about the underlying structure, and tended


to invent nonrepresentational rules. The results have strong implications


for a theory of implicit learning which stresses a nonconscious abstraction


system that operates when the stimulus environment exhibits exceedingly


complex structure, and subjects are not actively trying to break the code. Elsewhere implicit learning has been characterized as a process whereby a subject becomes sensitive to the structure inherent in


in a complex array by developing (implicitly) a conceptual model which reflects


the structure to some degree (Reber, 1967,


1969; Reber & Millward, 1968). It has


been a working hypothesis that the learning


process is fundamentally an abstraction of


information from the environment by the


subject without recourse to explicit strategies for responding or explicit systems for


encoding the stimuli. Support for the hypothesis has been indirect, consisting essentially of the failure of several groups of


researchers to find evidence of verbalizable,


explicit hypothesis testing or rule formation


even when subjects display highly efficient


exploitation of the patterns present in the


stimuli (Braine, 1963; Foss, 1968; Reber,


1967, 1969; Smith & Braine, in press).


Given the highly abstract nature of the


implicit learning process the evidence will


necessarily continue to be indirect. However, such evidence has a way of accumulating until a threshold of legitimacy is


achieved for the hypothesized abstraction.


This research was supported in part by grant


MH-20239-01 from the National Institute of Mental Health.


Requests for reprints should be sent to Arthur S.


Reber, Department of Psychology, Brooklyn College of CUNY, Brooklyn, New York 11210. This study provides an additional quantum


of data.


One of the obligations incurred whenever


a process is hypotheized is the specification


of the boundary conditions, the circumstances under which the presumed process


will occur. One limiting condition in implicit learning clearly must be the lower


bound on the complexity of the underlying


structure that defines the stimuli. That is,


the stimulus patterns must be such that they


bias against the possibility of there being


appropriate coding schemes available to the


subjects. The issue is essentially one of definition : If the subject can discover and


formalize the rule system that characterizes


the stimulus array then the experiment is


no longer an experiment in implicit learning; it becomes one in inductive rule learning or rule identification. It was this consideration that prompted the highly complex


structures in the past and dictates the similarly complex system in this study.


What is not so obvious, however, is the


question of the "set" of the subject when


confronted with these complex arrays. In


previous work great care has been taken to


ensure that subjects were neutral with regard to underlying structure, and thus presumably neutral with respect to the use of


explicit hypothesis-testing strategies. This


study was designed to explore the impact


that the operating set has upon the subject's


performance. The procedure used was IMPLICIT LEARNING OF SYNTHETIC LANGUAGE straightforward: The behavior of subjects


run under neutral instructions was compared with that of subjects who were given


general information about synthetic grammars and encouraged to undertake an explicit search for rules. More specifically,


the aim was to explore the differences between (a) subjects who maintained a relatively naive stance with regard to rule structure and operated in a neutral mode insofar


as the formulation of hypotheses and strategies is concerned, and (b) subjects who


actively searched for rules and operated in


an explicit hypothesis-testing mode.




Stimulus Items


As in previous studies the stimulus items were


strings of letters generated by a finite-state grammar (Figure 1). This grammar may be characterized as a Markovian process in which each


permissible transition from any one state, Si, to


another state, Sj, generates a symbol. A grammatical string in the language is defined as any


sequence of permissible transitions leading from


the initial state, So, to the terminal state,So'. The


language is defined as all possible paths through


the system. For example, the sequence of states


So-Sa-Sa-Si-Sz-Ss-Si-So' generates the acceptable


sequence VXVPXVS. This particular grammar


generates exactly 43 permissible letter sequences


of lengths three through eight which were used as


the grammatical stimulus materials for the experiment. (See Chomsky & Miller, 1958, and Reber,


1967, for details of the procedures involved in these


calculations.) Subjects


The subjects were 20 undergraduates who served


as part of a course requirement. They were randomly assigned to the two groups. Procedure


The experiment was run in two parts, a learning phase and a testing phase. Prior to the beginning of the learning phase, subjects were read


the instructions appropriate for the group they


were in. Except for these instructions all subjects were run identically.


The instructions for the implicit group (Group


I) were as follows:


This is a. simple memory experiment. You will


see items made up of the letters PSTVX. They


will run from three to eight letters in length


and will be shown to you in groups of three


items each. After seeing each set of three items


I will give you a card and your task will be to 89 FIGURE 1. Schematic diagram of the finite-state


grammar used to generate the stimuli. (So = initial


state; So' = terminal state. The language is all


possible paths through the system.)


try to reproduce all three items. I will tell


you which ones you reproduced correctly. After


you have reproduced all three correctly two


times in a row we will go on to a new set of


three items.


The instructions for the explicit group (Group


E) were the same as above with the addition of


the following:


The order of letters in each item is determined


by a rather complex set of rules. The rules only


allow certain letters to follow other letters. Since


the task involves memorization of a large number of these complex strings of letters, it will be


to your advantage if you can figure out what the


rules are, which letters may follow other letters


and which ones may not. Such knowledge will


certainly help you in learning and memorizing


the items.


Learning. Fifteen of the 43 grammatical strings


were selected as stimulus items for this phase of the


experiment. They were selected as representative


of the possible types of grammatical strings; for


example, for each length, strings beginning with


both T and V were included, and for all lengths


where it was possible, an example of each of the


loops of the grammar (P, X, VPX) was used.


These 15 items were presented to subjects in five


sets of 3 items each. Each item was printed on a


separate card and presented through a viewing window for 5 sec. After the 3 items of a given set


were shown, the subject was given a card and


asked to reproduce all 3 items. There were no


time restrictions on the subjects although long response times were rare. Subjects were told which


items were correctly reproduced but no information


was given about the nature of the errors. Each


set was run repeatedly in the same order until the


criterion of two consecutive correct reproductions


was reached, after which the next set was presented. The order of presentation was varied randomly for each subject. All subjects continued


until all 15 items were learned. A S-min rest ARTHUR S. REBER 90 TABLE 1






M Group I


E 8.3


17.4 4.9


9.5 4.0


7.7 2.0


5.3 2.3


4.5 4.03


8.88 Note. Abbreviations: I = implicit instructions; E = explicit


instructions. period was allowed before the beginning of the


test phase.


Testing. Twenty-two of the remaining grammatical items were selected along with 22 items


that violated the rules of the grammar. Four of


these nongrammatical items were formed randomly


and contained multiple violations; the remaining


18 contained only single-letter violations.


The stimulus items were presented through the


same viewing window. The subjects' task was to


make a decision about the correctness or grammaticality of each item based upon what they had


learned during the initial memorization phase and


to then press one of two buttons marked "yes"


and "no." Note that none of the subjects had been


told at the outset that there would be a testing


phase, and that for the subjects in Group I this


was the first time that any reference to rules had


been made.


The list of 44 test items was presented twice,


making a total of 88 for each subject. All subjects were informed about the equal proportions of


grammatical and nongrammatical items. There was


no time limit on the subjects during this phase


although they were told that latencies were being


recorded. No feedback about the correctness of a


decision was given until the full set of 88 items


was completed. RESULTS1 is high. Overall, Type 1 strings 2 were


somewhat easier to learn than other types,


and this tendency was observed in both


groups. The other four types were equally


difficult although Group E subjects experienced uniformly more difficulty than Group


I subjects and made more error on all types.




Note first that the lack of feedback about


the correctness of a response served to keep


subjects at a constant level of performance


throughout the testing phase. Also, as in


previous work, subjects were generally not


aware of the fact that each test item was


presented twice. Thus in the following


analyses the full set of 88 test items is


treated as a single block. The data are


presented in Table 2.










Response NG Total Group I




NG 334


106 99


341 433


447 Group E




NG 263


177 131


309 394


486 Learning Note. Group differences are highly significant, p < .001; correct responses for both groups are significantly better than


chance, fa < .001. Abbreviations; G = grammatical; NG


= nongrammatical; I = implicit instructions; E = explicit


instructions. The learning phase data are most easily


expressed in terms of errors to criterion. As


Table 1 shows there was a strong difference


between the groups with those given explicit


instructions performing significantly poorer,


*(18) = 4.75, SEAltt = 2.14. Trials to criterion data were comparable and are not




In general the learning data were similar


to those found in earlier work (Reber, 1967,


1969). Subjects in both groups eventually


adopted the procedure of focusing upon only


one or two strings per memorization trial,


a common strategy when information load 1


The rejection region throughout is p < .01


unless otherwise noted.




The finite-state grammar in Figure 1 generates


five basic sentence types. Each type is defined by


a path through the system with obligatory and


optional transitions, the latter being the loops


or recursions. The five types are as follows:


(1) T[P]TS; (2) T[P]TX[X](VPX[X])VS;


(3) T[P]TX[X](VPX[X])VPS; (4) V[X]


(VPX[X])VS; and (5) V[X] (VPX[X])VPS.


Note that Types 2 and 3 and Types 4 and 5 are


very similar differing only in the obligatory P in


the next-to-last position in Types 3 and 5; Type 1


appears, superficially, to be considerably simpler




Both groups showed the fruits of the


learning phase and were able to discriminate


grammaticality (G) from nongrammaticality


(NG) at far better than chance levels, fs(9)


= 22.3 and 15.2 for Groups I and E, respectively, SEM = .59 and 1.54, respectively.


Moreover, every one of the 20 subjects


showed discriminability above chance.


_ However, the two groups differed from each


other considerably in this ability, with Group


E subjects being poorer than those given


the implicit instructions, <(18) = 6.24, SEAUt


= 1.74. Group I performance agreed nicely


with earlier findings and, indeed, is actually


a replication of work reported in Reber


(1967); Group E performance fell far below this level. Note also that Group E


shows a strong bias toward nongrammatical


responses. The argument is made later in


this article that this bias can be expected


under the assumption that the primary effect


of the specific instructions to these subjects


was to produce a tendency for them to develop rule systems which were not representative of the underlying structure.


A variety of other, more fine-grain,


analyses were carried out, none of which


provided any important insight into the


qualitative differences between the two


groups, and all of which were comparable to


similar results found in Reber (1967). For


example, the nongrammatical items with the


violation in either the initial or the terminal


letter were detected better than items with


the violation in internal positions. Items


which contained multiple violations were


easier to detect as nongrammatical than


items with single letter violations. The five


item types were all equally likely to be recognized as grammatical. Naturally, the overall level of performance in all these cases


was lower for Group E subjects but the


general pattern was the same in both groups.


Further, the latency data failed to reveal


group differences. Both groups had shorter


latency distributions for items on which


grammaticality was correctly assessed than


on items where an error was made, p < .01


for Group I and p < .02 for Group E (Kolmogorov-Smirnov test). There were no differences in response time to assign grammaticality as opposed to nongrammaticality 91 for either group, and there were no betweengroups differences.


The one statistic that does reveal an important quantitative difference between the


mode of operation of the two groups is the


probability of a subject making an error on


both presentations of a given item (Pe,e)


compared with the probability of an error


on only one of the two presentations (Pe,c


and Pc,e). In terms of a simple detection


model, Pc,c = fc + (1-*) g2


Pc,e = P e , c = ( l - £ ) < 7 ( l - 0 ) P... = (!-*) ( l - < 7 ) 2 , where k is a parameter which reflects the


subjects' level of apprehension of the grammatical relations, that is, the probability of


knowing the grammaticality of any given


item. The probability of a correct guess


is represented as g, and by virtue of the


equal proportions of G and NG items, g =


.50. The known value of P<.tC was used to


estimate k and thus "predict" the other




In principle, k can be estimated from any


of the equations, although Pc,c is the appropriate source since this value alone is


assumed to contain instances where the subjects knew the grammatical status of the


letter strings. Further, estimating k in this


manner provides for a cleaner test of the


strong prediction of the model, that is,


Pc,e = Pe,e = Pe,e> which IS 3 direct result of fixing g at .50.


Interpreting the model is straightforward:


It is deemed appropriate only under the


condition where the subjects' decisions are


based upon an accurate (although partial)


representation of the rules of the grammar.


Inappropriate representative rules will create


instances where subjects incorrectly assume


that they know the grammatical status of


test items and will produce an inflated value


of Pe,e relative to Pc,e and Pe,c. Thus the


model serves as a sensitive test of the implicit nature of the subjects' behavior as it


pertains to the error data.


In evaluating the model x2 tests for goodness-of-fit were carried out with the values


of PeiC and Pc,e pooled. The pooling was


done so that any perturbations produced by 92 ARTHUR S. REBER








Result Group I






Group E




Obtained .66


.66 .11


.10 .11


.11 .11


.13 .53


.53 .16


.12 .16


.12 .16


.23 vaue o


= .55 and .37 for Groups


I and E, respectively.


model is rejected for


Group E. x!(10) = 44.06, p < .001, bu


not for Group I, x2(10) = 8.86. fluctuations in these values would not contribute to x2, and thus any rejection of the


model must be due to the inflated value


of Pe,e- These fluctuations were, of course,


nonsystematic, as can be seen from the


group averages given in Table 3 where Pe,c


and Pc,e are essentially identical for both




Each individual subject was tested against


the model and the individual x2 values were


summed to produce the group results shown


in Table 3. Using p < .05 as the critical


value, 7 of the 10 subjects in Group E had


values of Pe,e in excess of what would be


expected by chance alone, while only 1 such


subject was found in Group I. For completeness, all predicted and obtained values


are given in Table 3, although the x2 tests


were all run with the pooling described




The overall effects are quite clear. The


model is clearly an inappropriate characterization of the behavior of Group E subjects


while for Group I it is well within the


expected range. Subjects who operate in


an implicit mode develop abstract representations which are accurate (if partial) reflections of the structure of the stimulus


items. Subjects who are instructed to perform explicit rule searches develop abstractions which also contain representations


that are inaccurate reflections of the underlying structure. It is also worth noting that,


independent of the appropriateness of the


rule systems developed, both groups are


equally consistent in applications. Summing the values of .Pc,c and Pe,e yields .79 for


Group I and .76 for Group E, so that in


some sense each group has learned the same


"number" of rules.


Careful subject-by-subject analyses were


carried out in an effort to pinpoint qualitative


differences, particularly on those items responded to consistently. Occasionally systematicity was discovered, such as one E


subject who (erroneously) rejected any


item with a letter repeated more than four


times (despite the fact that TPPPPPTS


was among the set of learning items), and


another who accepted (also erroneously)


any item where the P-cycle was misplaced


(e.g., TTPPPXVS). These cases, however, were relatively rare and account for


a trivial amount of the large group differences that were found. The rich and


complex rule system which, as is argued


later, is necessary to produce implict learning, carries with it the serious liability that


subjects' abstractions will be similarly rich,


complex, and intractable.




There are several straightforward conclusions to be drawn from these data: (a) Subjects who engaged in an explicit search for


rules that define a complex structure performed more poorly in memorizing exemplars of the structure than subjects who


operated in a more neutral, implicit fashion,


(b) Although taken to the same learning


criterion, subjects operating in the explicit


mode ultimately learned less about the


underlying properties of the complex stimuli


than subjects in the implicit mode, (c) Explicit search for rules produced a strong


tendency for subjects to induce or invent


rules which were not accurate representations of the complex stimulus structure; this


tendency was not observed in subjects given


the implicit instructions.


The critical word in each of the foregoing


conclusions is complex. Except for it, these


conclusions would be at variance with the


rule-learning literature in which instructions that focus the subject's attention upon


the rule system generally accelerate learning.


In these other typically explicit rule-learning


experiments, since the stimulus patterns are IMPLICIT LEARNING OF SYNTHETIC LANGUAGE relatively simple and codable, a subject with


a reasonably rich stock of heuristics and


problem-solving strategies is going to find


their implementation rewarded. For example, essentially all of the work in serial


pattern learning (see Jones, 1974) has been


carried out using stimulus sequences whose


underlying structures can be coded by a subject equipped with search strategies based


upon devices like alternating events, eventrun length, complementation, and so forth.


However powerful these strategies may


be, the structure of the strings of letters


generated by the grammar in Figure 1 is


simply too rich to be coded by a subject


using them in the short time allowed. The


implication is that the explicit instructions


disrupted performance by inviting, indeed


encouraging, subjects to engage in futile


rule-search procedures and to elaborate rule


systems which were frequently nonrepresentational. The slower learning rate, the bias


toward assigning nongrammaticality, and the


inflated value of Pe,e all support this interpretation. It is important to note that the


instructions to Group E are not specifically


misleading. They interfere with performance, not because they mislead the subjects, but because they put them in an


operating set where they mislead themselves.


For example, one not atypical subject exhibited consternation during the postexperimental debriefing when she discovered that


her elaborate and sophisticated efforts to


find remote, deterministic contingencies between letters were doomed to fail. It is


trivially true, then, that searching for rules


will not work unless you can find them.


It should be emphasized here that explicit


rule search can jeopardize its user in


another, perhaps more significant, way. In


addition to producing poor performance because of a failure to find well-formed rules,


engaging in explicit rule search acts to mask


the implicit learning process. As has been


suggested elsewhere (Reber, 1967; Reber,


& Millward, 1968), the implicit acquisition


process seems to be most effective when the


subjects are in a relatively neutral, passive


set and allow themselves to be inundated by


the stimulus materials. The efforts on the


part of Group E subjects to break the code 93. precludes the operation of this implicit mode.


A serious difficulty with these experiments


on implicit learning is determining just how


they blend in with the traditional work on


rule learning and rule identification. Although this study is indeed an investigation of the conditions under which subjects


acquire rule-governed behaviors, it seems


prudent at this stage to allow the term


implicit learning to maintain definitional


integrity apart from both rule learning and


rule identification, as those terms are traditionally used. The separation from rule


identification seems straightforward; the


process here is basically one of systematic


testing of existing hypotheses and rules. It


is an interesting problem but one very different from inquiring how those rules came


to be.


The argument for the nonsynonymity of


implicit learning and rule learning is subtler.


As a first approximation it is proposed that


rule learning subsumes at least two elementary processes: a primitive process of


apprehending structure by attending to frequency cues, and a more explicit process


whereby various mnemonics, heuristics, and


strategies are engaged to induce a representational system. The former is what is


defined here as implicit learning, and it has


certain conceptual similarities with the




component of perceptual


learning (see Gibson, 1969). The latter


is what is listed in the psychological lexicon


under rule learning.


The paradigmatic confusion, however, is


not necessarily lessened by this classification. .In practice it is exceedingly difficult


to identify the boundary between rule learning and rule identification. I know of no


published report on rule learning in adults


where the rules to be learned were not immediate or trivial generalizations of well-practiced and readily retrievable rule systems.


Despite the nomenclature, cognitive psychologists rarely study rule learning.


The work of Miller and his associates on


artificial-grammar learning (Miller, 1967,


chap. 7) is illustrative. Their work, which


was begun using grammars of a complexity


approaching that of Figure 1 (Miller, 1958;


Shipstone, 1960), was shifted over to rela- ARTHUR S. REBER


tively simple systems on the grounds that


the synthetic languages used initially were


too intricate for "an afternoon in the laboratory." The outcome of "Project Grammarama" thus became, as Miller recognized,


an interesting procedure for evaluating the


process of explicit-rule induction. Essentially nothing was learned about the implicit


acquisition of highly complex systems like


languages. The problem was th...


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